哈尔滨工业大学学报  2019, Vol. 51 Issue (5): 1-6  DOI: 10.11918/j.issn.0367-6234.201808158 0

### 引用本文

FENG Shumin, NIAN Dong, ZHAO Hu. Parking network connectivity method based on attraction relationship[J]. Journal of Harbin Institute of Technology, 2019, 51(5): 1-6. DOI: 10.11918/j.issn.0367-6234.201808158.

### 文章历史

Parking network connectivity method based on attraction relationship
FENG Shumin, NIAN Dong, ZHAO Hu
School of Transportation Science and Engineering, Harbin Institute of Technology, Harbin 150090, China
Abstract: In order to research the topological structure of parking network, a new method of parking network connection based on attractive relationship is proposed. By improving the existing parking demand forecasting model and considering the factors such as parking utilization, parking turnover, and urban center index, a new parking demand algorithm is established. Drawing a conventional Voronoi diagram and using the key properties of the fracture point theory and physical parameters of the conventional Voronoi diagram to divide the influence range of each parking lot, the concept of attractiveness is introduced, and the parameters in the formula of attractiveness calculation are modified and calibrated. The degree of attraction of each node in the parking lot network and the edge weight of the connection between nodes were calculated. Then the connection situation between parking lots was determined. In this paper, the model of parking lot network is constructed under the attraction relationship of some parking lots in Harbin. The complex network parameter analysis method is adopted to analyze the parking lot network. The macro and micro node parameters of parking lot under the scale assignment calculation method and further calculation through the attraction relation are analyzed. Results show that the parking lot network degree distribution conforms to the power law distribution, and the scale-free network features are obvious. Compared with the global coupled network and the unilateral coupled network, the parking lot network under the attraction relationship has a high clustering coefficient and global effectiveness and a small average path length. This method is a practical mode whose performance exhibits equilibrium under the existing parameter evaluation system.
Keywords: urban traffic     complex network     attractiveness     parking lot     Voronoi diagram     break point theory

1 参数计算 1.1 停车场需求确定

 ${S_i} = \alpha \mathop \sum \limits_{j = 1}^m {P_{ij}}{d_j}.$ (1)

 ${S_i} = \frac{{\alpha \mathop \sum \limits_{j = 1}^m {P_{ij}}{d_j}}}{{{U_i}{\omega _i}}}\left( {i = 1, 2, \ldots , n} \right).$ (2)

 ${S_i} = \frac{{{\alpha _i}\mathop \sum \limits_{j = 1}^n {d_j}{P_{ij}}}}{{{U_i}{\omega _i}}}\varepsilon \delta \sigma .$ (3)

1.2 参数标定

1) ε与停车场地理位置, 规模等有关.调整停车场费率是一种有效控制小汽车出行的手段.ε是一种对费率进行折算的方式, 其计算方式为

 $\varepsilon = \frac{{{P_{\rm{r}}}}}{{{P_{\rm{e}}}}}.$ (4)

2) 停车场服务水平是能够直接影响驾驶人停车体验的参数, 其计算公式为

 $\delta = 1 + \frac{{A - {S_i}}}{A}.$ (5)

3) 中心性强度是停车场对区域影响能力的综合要素, 是停车场划分服务范围和影响空间的依据.中心性强度可利用主成分分析法进行定量刻画, 其计算方法为

 $\delta = \sum {V_i}{C_i}.$ (6)

4) ωi为在单位时间内, 单个停车位被使用的次数.当单个泊位使用次数较多时, 提供少量车位即可满足停车需求.其计算方式为

 ${\omega _i} = \frac{{\sum t}}{{60A}}\left( {i = 1, 2, \cdots , n} \right).$ (7)

5) 停车场周转率与停车时间及利用率有关.在一定情况下, 周转率越大, 停车需求越小.周转率的计算公式为

 ${U_i} = \frac{{{L_{\rm{o}}}}}{{{L_i}}}\left( {i = 1, 2, \cdots , n} \right).$ (8)

 ${\mu _n} = \left\{ \begin{array}{l} {n^\mu }, n = 0, 1, 2, \cdots ;\\ {c^\mu }, n = {\rm{c}} + 1, {\rm{c}} + 2, \cdots . \end{array} \right.$ (9)

 ${A_n} = \left\{ \begin{array}{l} \frac{{{c^n}{\mu ^n}}}{{n!(\mathop \sum \limits_{n = 0}^{c - 1} \frac{{{{\left( {\mu c} \right)}^n}}}{{n!}} + \frac{{{{\left( {\mu c} \right)}^c}}}{{c!\left( {1 - \mu } \right)}})}}, 0 \le n \le c;\\ \frac{{{c^n}{\mu ^n}}}{{c!(\mathop \sum \limits_{n = 0}^{c - 1} \frac{{{{\left( {\mu c} \right)}^n}}}{{n!}} + \frac{{{{\left( {\mu c} \right)}^c}}}{{c!\left( {1 - \mu } \right)}})}}, n \ge c. \end{array} \right.$ (10)
1.3 停车场影响范围确定

1) 相邻城市之间的欧氏距离等于断裂点到两个城市欧氏距离之和.公式表示为

 ${d_{\rm{A}}} + {d_{\rm{B}}} = {D_{{\rm{AB}}}}.$ (11)

2) 断裂点到相邻两个城市的距离与这两个城市中心强度(规模)的平方根成正比.用公式表示为

 ${d_{\rm{A}}} = {D_{{\rm{AB}}}}/(1 + \sqrt {{P_{\rm{B}}}/{P_{\rm{A}}}} ),$ (12)
 ${d_{\rm{B}}} = {D_{{\rm{AB}}}}/(1 + {P_{\rm{A}}}/{P_{\rm{B}}}).$ (13)

3) 每个城市的权重等于其中心性强度值的平方根.

2 吸引关系介绍

 ${R_{{\rm{ab}}}} = {k_{{\rm{ab}}}}\frac{{{P_{\rm{a}}}{P_{\rm{b}}}}}{{{L^2}}},$ (14)
 ${K_{\rm{a}}} = \mathop \sum \limits_{b = 1}^n {R_{{\rm{ab}}}}.$ (15)

 ${R_{{\rm{ab}}}} = \sum k{'_{{\rm{ab}}}}\frac{{{P_{\rm{a}}}{P_{\rm{b}}}}}{{{T^2}}}.$ (16)

 ${T_{\rm{a}}} = {W_{\rm{a}}} - \frac{{{\alpha _{\rm{a}}}\mathop \sum \limits_{j = 1}^n {{\rm{d}}_j}{P_{{\rm{a}}j}}}}{{{U_{\rm{a}}}{\omega _{\rm{a}}}}}{\varepsilon _{\rm{a}}}{\delta _{\rm{a}}}{\sigma _{\rm{a}}}.$ (17)

 ${R_{{\rm{ab}}}} = \frac{{k'{'_{{\rm{ab}}}}{M_{\rm{a}}}{M_{\rm{b}}}}}{{{T^2}}}.$ (18)

 $k'{'_{{\rm{ab}}}} = \left\{ \begin{array}{l} 1, {l_{{\rm{ab}}}} \le {l_{\min }};\\ 1 - {(\frac{{{l_{{\rm{ab}}}} - {l_{\min }}}}{{{l_{\max }} - {l_{\min }}}})^\beta }, {l_{\min }} \le {l_{{\rm{ab}}}} \le {l_{\max }};\\ 0, {l_{\max }} \le {l_{{\rm{ab}}}}. \end{array} \right.$ (19)

3 实例解析

3.1 常用网络介绍

 图 1 两种耦合网络制式 Fig. 1 Two coupling network formats
3.2 规模赋值下的停车场网络模型

 图 2 规模赋值下的停车场网络连接 Fig. 2 Parking network connection under the assignment of scale

 $y = 15.221{x^{ - 0.707}}.$ (20)

3.3 吸引关系下的停车场网络模型

 图 3 停车场常规Voronoi Fig. 3 The regular Voronoi diagram of the parking lot

 图 4 吸引关系下的停车场网络连接 Fig. 4 Parking network connection under the attraction relationship

 $y = 24.892{x^{ - 0.648}}.$ (21)

4 结论

1) 确定了停车场之间的吸引度表达式, 并利用停车需求量确定停车场规模, 通过吸引度参数确定停车场之间是否进行连接, 从而构建停车场网络模型.

2) 提出了一种通过Voronoi图的面积划分确定停车场吸引度的停车场连接方式.通过引进服务水平影响率, 地区中心性指数等指标确定了新的停车场规模计算模型, 并将其代入改进的断裂点计算公式中, 评估每个停车场节点的吸引度及停车场之间的连接关系.

3) 通过哈尔滨市南岗区的停车场网络实例可以得出:在仅通过停车场规模确定停车场间连接关系时可以获得较为简洁有效的停车场网络, 但由于节点度较小造成了网络的选择性差.通过Voronoi图计算的停车场网络在拥有较大节点度的同时保证了路径长度, 聚类系数和全局有效性.

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